189 results on '"incident detection"'
Search Results
2. IDILIM: incident detection included linear management using connected autonomous vehicles.
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Gokasar, Ilgin, Timurogullari, Alperen, Ozkan, Sarp Semih, and Deveci, Muhammet
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MACHINE learning , *GENERATIVE adversarial networks , *CONVOLUTIONAL neural networks , *DEEP learning , *VEHICLE detectors - Abstract
Autonomous vehicle advancements and communication technologies such as V2V, V2I, and V2X have enabled the development of connected and autonomous vehicles. Because CAVs are directly effective in traffic, their application in traffic management and incident management appears promising. They can immediately begin regulating traffic and acting as sensors due to their connectivity to the infrastructure. This research proposes Incident Detection Included Linear Management (IDILIM), a CAV-based incident management algorithm that regulates CAV and traffic speeds based on dynamic and predicted shockwave speeds. The SUMO simulations are carried out on a 10.4-km-long, three-lane facility with 21 sensors every 500 m. In the scenarios, three traffic demands, eleven CAV penetration rates, and varying incident locations, duration, and lanes are used. A total of 20 simulation seeds are used in each scenario. The proposed algorithm necessitates the use of a reliable traffic prediction model. Convolutional Neural Networks, a deep learning algorithm with high estimation accuracy, are used in the prediction model. IDILIM uses the highly accurate traffic prediction output of the Pix-to-Pix model as input at 3-min intervals. Shockwave speed is calculated using model outputs and fed to CAVs. To compare with IDILIM, variable speed limits (VSL) are also modeled. When compared to uncontrolled base scenarios, IDILIM reduced density values greater than 35 veh/km in the critical region by 89.32%. In the same scenario, VSL management decreased by only 52.43%. [ABSTRACT FROM AUTHOR]
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- 2024
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3. Algorithm Research on Freeway Incident Recognition and Risk Prediction
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Wang, Shuguo, Wang, Zhonghua, Zhen, Xuming, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, Wang, Wuhong, editor, Guo, Hongwei, editor, Jiang, Xiaobei, editor, Shi, Jian, editor, and Sun, Dongxian, editor
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- 2024
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4. Incident detection on urban roads - A case study using synthetic floating car data.
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Fuchs, Lea, Grau, Josephine, Baumann, Marvin V., Weyland, Claude M., and Vortisch, Peter
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FLOW simulations ,TRAFFIC flow ,DATA quality ,SOFTWARE development tools ,SIMULATION software ,SPEECH synthesis - Abstract
Within the scope of this paper we examine incident detection on inner-city roads using floating car data (FCD) under different traffic conditions and data qualities. We used synthetic FCD to analyze incidents under variable conditions, such as different traffic volumes. They are generated from microscopic traffic flow simulations with the software tool PTV Vissim. Automated processing of the synthetic FCD gained from the microsimulation models also makes it possible to analyze different data qualities resulting from the choice of penetration rate and transmission frequency. We investigated the recognizability of the incidents using five laboratory examples, which depict different traffic incidents, and a real inner-city road in Karlsruhe, Germany. Graphical and quantitative evaluations are used for incident detection, whereby violin plots and the proposed incident indicator have the most reliable detection rate. Incident detection decreases significantly if several incidents overlap, the severity of the incidents or the traffic volume is reduced, the data quality decreases, or if there is no current speed information in the data. For additional insights, we recommend considering the different figures created in the graphical analysis alongside the incident indicator used in the quantitative analysis. [ABSTRACT FROM AUTHOR]
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- 2024
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5. An AutoML-based approach for automatic traffic incident detection in smart cities.
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Gkioka, Georgia, Dominguez, Monica, and Mentzas, Gregoris
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TRAFFIC monitoring ,SMART cities ,INTELLIGENT transportation systems ,MACHINE learning ,METROPOLIS - Abstract
In the realm of modern urban mobility, automatic incident detection is a critical element of intelligent transportation systems (ITS), since the ability to promptly identify unexpected events allows for quick implementation of preventive measures and efficient response to the situations as they arise. With the growing availability of traffic data, Machine Learning (ML) has become a vital tool for enhancing traditional incident detection methods. Automated machine-learning (AutoML) techniques present a promising solution by streamlining the machine-learning process; however the application of AutoML for incident detection has not been widely explored in scientific research In this paper, we propose and apply an AutoML-based methodology for traffic incident detection and compare it with state-ofthe-art ML approaches. Our approach integrates data preprocessing with AutoML, and uses Tree-based Pipeline Optimization Tool (TPOT) to refine the process from raw data to prediction. We have tested the efficiency of our approach in two major European cities, Athens and Antwerp. Finally, we present the limitations of our work and outline recommendations for application of AutoML in the incident detection task and potentially in other domains. [ABSTRACT FROM AUTHOR]
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- 2024
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6. A New Approach to Road Incident Detection Leveraging Live Traffic Data: An Empirical Investigation.
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Kumar Gannina, Aswin Ram, Jaffarullah, Aadhil Ahamed, Reddy, Tiyyagura Mohit, Subba Reddy, Sabbella Manoj, Vikas, Ambati Sai, Mathi, Senthilkumar, and Ramalingam, Venkadeshan
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TRAFFIC monitoring ,TRAFFIC accidents ,LAW enforcement agencies ,TRAFFIC patterns ,ROAD closures - Abstract
Accidents can have a significant impact on road safety and the efficiency of transport. With the ever-increasing number of vehicles on the road, it is crucial to quickly detect and respond to such events to decrease the impact. The current paper presents a road accident and incident detection solution utilizing traffic API monitoring and continuously analyzing real-time traffic data from various sources like GPS-enabled devices. Consequently, the proposed approach can identify abnormal patterns in the movement of vehicles, such as unexpected road closures or an accident. It numerically analyzes the traffic pattern and identifies incidents and accidents using live traffic data collected from the map service. When the approach identifies any abnormality, it can immediately let the user know, allowing them to act accordingly. It can facilitate authorities in identifying accident-prone areas and deploy safety measures. This can help law enforcement agencies to identify drivers at risk of causing accidents and implement education and enforcement programs targeted towards them. Hence, the proposed approach can aid in reducing the negative impact of traffic incidents, and the public can respond to these incidents more preparedly. [ABSTRACT FROM AUTHOR]
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- 2024
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7. Designing a Human-centered AI Tool for Proactive Incident Detection Using Crowdsourced Data Sources to Support Emergency Response.
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Senarath, Yasas, Mukhopadhyay, Ayan, Purohit, Hemant, and Dubey, Abhishek
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ARTIFICIAL intelligence - Abstract
Time of incident reporting is a critical aspect of emergency response. However, the conventional approaches to receiving incident reports have time delays. Non-traditional sources such as crowdsourced data present an opportunity to detect incidents proactively. However, detecting incidents from such data streams is challenging due to inherent noise and data uncertainty. Naively maximizing detection accuracy can compromise spatial-temporal localization of inferred incidents, hindering response efforts. This article presents a novel human-centered AI tool to address the above challenges. We demonstrate how crowdsourced data can aid incident detection while acknowledging associated challenges. We use an existing CROME framework to facilitate training and selection of best incident detection models, based on parameters suited for deployment. The human-centered AI tool provides a visual interface for exploring various measures to analyze the models for the practitioner's needs, which could help the practitioners select the best model for their situation. Moreover, in this study, we illustrate the tool usage by comparing different models for incident detection. The experiments demonstrate that the CNN-based incident detection method can detect incidents significantly better than various alternative modeling approaches. In summary, this research demonstrates a promising application of human-centered AI tools for incident detection to support emergency response agencies. [ABSTRACT FROM AUTHOR]
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- 2024
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8. Road Incident Detection Under Rate Adaptation-Based Congestion Control in Cooperative Vehicular Systems
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Sandy Bolufe, Jorge F. Silva, Cesar A. Azurdia-Meza, Ismael Soto, and Sandra Cespedes
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Congestion control ,cooperative vehicular systems ,ETSI DCC ,incident detection ,lane-changing maneuver ,LIMERIC ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - Abstract
Cooperative vehicular systems require that vehicles fuse sensor data and broadcast one-hop safety messages containing their kinematic information to enable vehicular applications based on incident detection. Several congestion control mechanisms have been proposed to mitigate channel congestion resulting from the continuous transmission of safety messages. This paper investigates the effect of message rate adaptation-based congestion control from a road safety perspective by evaluating the feasibility of prominent approaches, such as PULSAR, LIMERIC, reactive ETSI DCC, and SAE J2945/1, to support lane-changing maneuvers on multi-lane highways under varying conditions. Simulation results demonstrate that message size, vehicular density, losses at the physical layer, and observation time significantly influence the lane-changing application’s capability to detect unsafe maneuvers when congestion control is in action. Specific recommendations and guidelines for congestion control are provided to improve decision-making at the application level.
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- 2024
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9. Management and Impact of COVID-19 on Intelligent Transportation System
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Tyagi, Amit Kumar, Sreenath, Niladhuri, Chatterjee, Prasenjit, Series Editor, Awasthi, Anjali, Series Editor, Tiwari, Manoj Kumar, Series Editor, Chakraborty, Shankar, Series Editor, Yazdani, Morteza, Series Editor, Tyagi, Amit Kumar, and Sreenath, Niladhuri
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- 2023
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10. Dynamic Adaptation of Activation Function to Fine Tune Video ResNet for Fight or Non-Fight Classification.
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Faridi, Atif, Siddiqui, Farheen, Nandan, Durgesh, Nafis, Md Tabrez, and Ahad, Mohd Abdul
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CONVOLUTIONAL neural networks ,VIDEOS - Abstract
The task of designing and training a 3D convolutional neural network (CNN) from scratch poses significant complexity, necessitating high levels of expertise to achieve a performance that rivals the state-of-the-art. To circumvent this, fine-tuning of neural networks has emerged as a formidable approach. This study focuses on the utilization of Video ResNet, a state-of-the-art architecture known for its proficiency in capturing spatiotemporal patterns from video data. A novel approach is proposed for the fine-tuning of the 3D CNN model (Video ResNet) that involves altering activation functions over epochs while maintaining the network weights and biases consistent. This dynamic approach was assessed under various hyperparameters, yielding encouraging results. Contrary to most studies that employ down-sampling of the temporal sequence to minimize memory requirements, this study introduces a sliding window-based approach to evade down-sampling and prevent potential information loss. The proposed methodology yielded an accuracy of 87.25% in the fight/non-fight classification on the RWF-2000 dataset, marginally surpassing the performance of the state-of-the-art model. The proposed method not only facilitates the development of a real-time video incident detection model but also addresses the issue of overfitting during training through the incorporation of adaptive dynamic activation functions. This study thus contributes to the ongoing advancements in the field of neural network fine-tuning and video data classification. [ABSTRACT FROM AUTHOR]
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- 2023
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11. What do riders say and where? The detection and analysis of eyewitness transit tweets.
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Kabbani, O., Klumpenhouwer, W., El-Diraby, T., and Shalaby, A.
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NATURAL language processing , *SENTIMENT analysis , *WITNESSES , *SOCIAL media , *PUBLIC transit - Abstract
Information shared on social media by transit system customers is often candid, localized, and includes in the moment information about emerging events or issues. Twitter provides an unfiltered and timestamped feed of information that can be aggregated to generate valuable insights. Our research aims to identify passenger-related transit incidents from a public Twitter feed. Detecting these incidents in real time enables transit agencies to immediately respond to them by dispatching security, safety, or maintenance crews or by rapidly replying to requests made to the agency that are urgent in nature. We leverage natural language processing to extract latent information from identified eyewitness tweets about transit, focusing on location details, topic classification, and sentiment analysis. We outline an approach to developing a useful corpus of transit-focused tweets, detecting in the moment events, classifying these tweets into topics, and detecting locations where possible. We then demonstrate the approach through an application to Calgary Transit in Calgary, Canada. The results demonstrate that key incidents can be identified and prioritized for an agency. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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12. The Role of Machine Learning in Cybersecurity.
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Apruzzese, Giovanni, Laskov, Pavel, Montes de Oca, Edgardo, Mallouli, Wissam, Brdalo Rapa, Luis, Grammatopoulos, Athanasios Vasileios, and Di Franco, Fabio
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MACHINE learning ,INTERNET security ,INFORMATION storage & retrieval systems ,ARTIFICIAL intelligence ,STAKEHOLDERS - Abstract
Machine Learning (ML) represents a pivotal technology for current and future information systems, and many domains already leverage the capabilities of ML. However, deployment of ML in cybersecurity is still at an early stage, revealing a significant discrepancy between research and practice. Such a discrepancy has its root cause in the current state of the art, which does not allow us to identify the role of ML in cybersecurity. The full potential of ML will never be unleashed unless its pros and cons are understood by a broad audience. This article is the first attempt to provide a holistic understanding of the role of ML in the entire cybersecurity domain—to any potential reader with an interest in this topic. We highlight the advantages of ML with respect to human-driven detection methods, as well as the additional tasks that can be addressed by ML in cybersecurity. Moreover, we elucidate various intrinsic problems affecting real ML deployments in cybersecurity. Finally, we present how various stakeholders can contribute to future developments of ML in cybersecurity, which is essential for further progress in this field. Our contributions are complemented with two real case studies describing industrial applications of ML as defense against cyber-threats. [ABSTRACT FROM AUTHOR]
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- 2023
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13. GPS-based incident detection algorithm for two-lane bus rapid transit systems: case study of Istanbul Metrobus
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Goncu, Sadullah and Sahin, Ismail
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- 2023
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14. Enhancing intelligent transport systems: A cutting-edge framework for context-aware service management with hybrid deep learning.
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Nagappan, G., Maheswari, K.G., and Siva, C.
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DEEP learning , *INTELLIGENT transportation systems , *INFORMATION & communication technologies , *CORAL reefs & islands , *QUALITY of service , *SUSTAINABILITY , *CORALS , *TRANSPORTATION safety measures - Abstract
This study presents a comprehensive framework for optimizing intelligent transport systems (ITS) by integrating advanced communication and information technologies into vehicles, roads, and infrastructure. The primary goal is to enhance transportation efficiency, safety, and environmental sustainability while improving overall mobility for people and goods. Leveraging contextual information, the framework offers personalized, proactive services such as real-time traffic updates, route recommendations, and parking availability. Additionally, it enhances safety and security by providing early hazard warnings and adapting to changing road conditions. Our proposed framework utilizes the enhanced coral reef optimization (ECRO) algorithm to efficiently group vehicles for energy-saving data collection, maximizing information gathering efficiency. Collected data is then transmitted to a central data gathering center via a sink node optimized through the modified pelican optimization (MPO) algorithm, considering various vehicle node design constraints. An incident detection module accurately classifies and detects road incidents, enabling timely emergency service requests and alternate route recommendations. To facilitate incident detection, we introduce the deep Rigdelet neural network (DRNN), a novel deep learning technique tailored for decision-making in incident classification. We validate our framework's performance through NS-2 simulations using the SUMO traffic generator, demonstrating its effectiveness in meeting quality of service (QoS) metrics. Through comparative analysis with existing frameworks, our proposed approach stands out for its superior performance and ability to optimize ITS operations. [ABSTRACT FROM AUTHOR]
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- 2024
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15. Variable-Length Multivariate Time Series Classification Using ROCKET: A Case Study of Incident Detection
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Agnieszka Bier, Agnieszka Jastrzebska, and Pawel Olszewski
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Classification ,incident detection ,multivariate time series ,ROCKET ,varying-length time series ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - Abstract
Multivariate time series classification is a machine learning problem that can be applied to automate a wide range of real-world data analysis tasks. RandOm Convolutional KErnel Transform (ROCKET) proved to be an outstanding algorithm capable to classify time series accurately and quickly. The textbook variant of the multivariate time series classification problem assumes that time series to be classified are all of the same length, while in real-world applications this assumption not necessarily holds. The literature of this domain does not pay enough attention to data processing pipelines for variable-length time series. Thus, in this paper, we present a thorough analysis of three preprocessing pipelines that handle variable-length time series that need to be classified with a method that requires the data to be of equal length. These three methods are truncation, padding, and forecasting of missing value. Experiments conducted on benchmark datasets, showed that the recommended procedure involves padding. Forecasting ensures similar classification accuracy, but comes at a much higher computational cost. Truncation is not a viable option. Furthermore, in the paper, we present a novel domain of application of multivariate time series classification algorithms, that is incident detection in cash transactions. This area poses substantive challenges for automated model training procedures since the data is not only variable-length, but also heavily imbalanced. In the study, we list various incident types and present trained classifiers capable to aid human auditors in their daily work.
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- 2022
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16. Using Text Messaging to Locate and Verify Incidents Outside of Traffic Management System Coverage Areas
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- 2024
17. Opportunities for Traffic Management Systems to Share Information on Incidents
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- 2024
18. Detecting Natural Disasters, Damage, and Incidents in the Wild
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Weber, Ethan, Marzo, Nuria, Papadopoulos, Dim P., Biswas, Aritro, Lapedriza, Agata, Ofli, Ferda, Imran, Muhammad, Torralba, Antonio, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Vedaldi, Andrea, editor, Bischof, Horst, editor, Brox, Thomas, editor, and Frahm, Jan-Michael, editor
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- 2020
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19. COMPASS Program
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Gentili, David and Volpe, Richard, editor
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- 2020
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20. Multiple Sensors Data Integration for Traffic Incident Detection Using the Quadrant Scan.
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Zaitouny, Ayham, Fragkou, Athanasios D., Stemler, Thomas, Walker, David M., Sun, Yuchao, Karakasidis, Theodoros, Nathanail, Eftihia, and Small, Michael
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TRAFFIC monitoring , *TRAFFIC congestion , *TRAFFIC flow , *DETECTORS , *DATA integration , *TIME series analysis - Abstract
Non-recurrent congestion disrupts normal traffic operations and lowers travel time (TT) reliability, which leads to many negative consequences such as difficulties in trip planning, missed appointments, loss in productivity, and driver frustration. Traffic incidents are one of the six causes of non-recurrent congestion. Early and accurate detection helps reduce incident duration, but it remains a challenge due to the limitation of current sensor technologies. In this paper, we employ a recurrence-based technique, the Quadrant Scan, to analyse time series traffic volume data for incident detection. The data is recorded by multiple sensors along a section of urban highway. The results show that the proposed method can detect incidents better by integrating data from the multiple sensors in each direction, compared to using them individually. It can also distinguish non-recurrent traffic congestion caused by incidents from recurrent congestion. The results show that the Quadrant Scan is a promising algorithm for real-time traffic incident detection with a short delay. It could also be extended to other non-recurrent congestion types. [ABSTRACT FROM AUTHOR]
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- 2022
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21. Present and Future of Network Security Monitoring
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Marta Fuentes-Garcia, Jose Camacho, and Gabriel Macia-Fernandez
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Network security ,NSM ,security monitoring ,incident detection ,incident response ,SDN ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - Abstract
Network Security Monitoring (NSM) is a popular term to refer to the detection of security incidents by monitoring the network events. An NSM system is central for the security of current networks, given the escalation in sophistication of cyberwarfare. In this paper, we review the state-of-the-art in NSM, and derive a new taxonomy of the functionalities and modules in an NSM system. This taxonomy is useful to assess current NSM deployments and tools for both researchers and practitioners. We organize a list of popular tools according to this new taxonomy, and identify challenges in the application of NSM in modern network deployments, like Software Defined Network (SDN) and Internet of Things (IoT).
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- 2021
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22. Incident detection and classification in renewable energy news using pre-trained language models on deep neural networks.
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Wang, Qiqing and Li, Cunbin
- Subjects
- *
ARTIFICIAL neural networks , *RENEWABLE energy sources , *ENERGY consumption , *RECURRENT neural networks , *CONVOLUTIONAL neural networks - Abstract
The surge of renewable energy systems can lead to increasing incidents that negatively impact economics and society, rendering incident detection paramount to understand the mechanism and range of those impacts. In this paper, a deep learning framework is proposed to detect renewable energy incidents from news articles containing accidents in various renewable energy systems. The pre-trained language models like Bidirectional Encoder Representations from Transformers (BERT) and word2vec are utilized to represent textual inputs, which are trained by the Text Convolutional Neural Networks (TCNNs) and Text Recurrent Neural Networks. Two types of classifiers for incident detection are trained and tested in this paper, one is a binary classifier for detecting the existence of an incident, the other is a multi-label classifier for identifying different incident attributes such as causal-effects and consequences, etc. The proposed incident detection framework is implemented on a hand-annotated dataset with 5 190 records. The results show that the proposed framework performs well on both the incident existence detection task (F1-score 91.4%) and the incident attributes identification task (micro F1-score 81.7%). It is also shown that the BERT-based TCNNs are effective and robust in detecting renewable energy incidents from large-scale textual materials. [ABSTRACT FROM AUTHOR]
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- 2022
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23. Learning Traffic as Images for Incident Detection Using Convolutional Neural Networks
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Xiaozhou Liu, Hengxing Cai, Renxin Zhong, Weili Sun, and Junzhou Chen
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Binary classification ,convolutional neural networks ,Gramian Angular Difference Fields ,incident detection ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - Abstract
The timely and accurate detection of traffic incidents is beneficial to reduce associated economic losses and avoid secondary crashes. Inspired by the impressive success of the image classification algorithms, especially convolutional neural networks (CNNs), this study proposes a novel framework to detect highway traffic incidents by learning the traffic state as images. In such a framework, the probe vehicles equipped with the global positioning system equipment are used to obtain data. The Gramian Angular Difference Fields and Piecewise Aggregation Approximation algorithms are used to convert the link speed time series data into images. CNNs can extract the traffic features based on these images and consider an incident detection problem as a binary classification task. Further, the effectiveness of the proposed framework is evaluated by applying it to detect the traffic in a real-world environment, i.e., the Guangzhou Airport Expressway. The results illustrate that the proposed model outperforms several other algorithms with respect to almost all the performance indexes, including the detection rate with different false alarm rates and the area under the receiver operating characteristic curve.
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- 2020
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24. VANET-based traffic monitoring and incident detection system: A review.
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Hamdi, Mustafa Maad, Audah, Lukman, Rashid, Sami Abduljabbar, and Alani, Sameer
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TRAFFIC monitoring ,INTELLIGENT transportation systems ,VEHICULAR ad hoc networks - Abstract
As a component of intelligent transport systems (ITS), vehicular ad hoc network (VANET), which is a subform of manet, has been identified. It is established on the roads based on available vehicles and supporting road infrastructure, such as base stations. An accident can be defined as any activity in the environment that may be harmful to human life or dangerous to human life. In terms of early detection, and broadcast delay. VANET has shown various problems. The available technologies for incident detection and the corresponding algorithms for processing. The present problem and challenges of incident detection in VANET technology are discussed in this paper. The paper also reviews the recently proposed methods for early incident techniques and studies them. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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25. Incident Detection in Industrial Processes Utilizing Machine Learning Techniques
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Tziroglou, Giorgos, Vafeiadis, Thanasis, Ziogou, Chrysovalantou, Krinidis, Stelios, Voutetakis, Spyros, Tzovaras, Dimitrios, Kacprzyk, Janusz, Series editor, Pal, Nikhil R., Advisory editor, Bello Perez, Rafael, Advisory editor, Corchado, Emilio S., Advisory editor, Hagras, Hani, Advisory editor, Kóczy, László T., Advisory editor, Kreinovich, Vladik, Advisory editor, Lin, Chin-Teng, Advisory editor, Lu, Jie, Advisory editor, Melin, Patricia, Advisory editor, Nedjah, Nadia, Advisory editor, Nguyen, Ngoc Thanh, Advisory editor, Wang, Jun, Advisory editor, Burduk, Anna, editor, and Mazurkiewicz, Dariusz, editor
- Published
- 2018
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26. Supporting the Human in Cyber Defence
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Helkala, Kirsi, Knox, Benjamin J., Jøsok, Øyvind, Lugo, Ricardo G., Sütterlin, Stefan, Dyrkolbotn, Geir Olav, Svendsen, Nils Kalstad, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Katsikas, Sokratis K., editor, Cuppens, Frédéric, editor, Cuppens, Nora, editor, Lambrinoudakis, Costas, editor, Kalloniatis, Christos, editor, Mylopoulos, John, editor, Antón, Annie, editor, and Gritzalis, Stefanos, editor
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- 2018
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27. Traffic Incident Detection Based on Dynamic Graph Embedding in Vehicular Edge Computing.
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Li, Gen, Nguyen, Tri-Hai, and Jung, Jason J.
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TRAFFIC monitoring ,EDGE computing ,TIME series analysis ,INTERNET of things - Abstract
With a large of time series dataset from the Internet of Things in Ambient Intelligence-enabled smart environments, many supervised learning-based anomaly detection methods have been investigated but ignored the correlation among the time series. To address this issue, we present a new idea for anomaly detection based on dynamic graph embedding, in which the dynamic graph comprises the multiple time series and their correlation in each time interval. We propose an entropy for measuring a graph's information injunction with a correlation matrix to define similarity between graphs. A dynamic graph embedding model based on the graph similarity is proposed to cluster the graphs for anomaly detection. We implement the proposed model in vehicular edge computing for traffic incident detection. The experiments are carried out using traffic data produced by the Simulation of Urban Mobility framework. The experimental findings reveal that the proposed method achieves better results than the baselines by 14.5% and 18.1% on average with respect to F1-score and accuracy, respectively. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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28. Detection of downhole incidents for complex geological drilling processes using amplitude change detection and dynamic time warping.
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Li, Yupeng, Cao, Weihua, Hu, Wenkai, and Wu, Min
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DRILLING & boring , *TIME , *CASE studies - Abstract
Geological drilling process is operating under complex geological conditions, which may lead to a high risk of downhole incidents and thus compromise the drilling efficiency. To achieve prompt detection of downhole incidents and prevent them from developing to serious drilling accidents, this paper proposes a new data-driven detection method for downhole incidents based on the amplitude change detection and dynamic time warping. Two major phases are involved: the change monitoring phase detects whether there is any significant change in the drilling signals and extracts variational trend features by linear fitting and amplitude change detection; the incident detection phase determines if the cause of a change is a normal switching or a downhole incident by similarity analysis based on the dynamic time warping and the density-based spatial clustering. Industrial case studies show that the proposed method achieves good performance in downhole incidents detection for geological drilling processes. • This work proposes a new data-driven method to detect downhole incidents. • A change monitoring approach is presented to detect signal amplitude changes. • A similarity analysis technique is developed based on DTW and DBSCAN. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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29. Face off: Travel Habits, Road Conditions and Traffic City Characteristics Bared Using Twitter
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Amit Agarwal and Durga Toshniwal
- Subjects
Incident detection ,social media ,Named Entity Recognition ,Part of Speech ,hotspot detection ,word embedding ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - Abstract
The adequacy of traditional transport related issues detection is often limited by physical sparse sensor coverage and reporting incident/issues to the emergency response system is labor intensive. The social media tweet text have been mined so as to identify the complaints regarding various road transportation issues of traffic, accident, and potholes. In order to identify and segregate tweets related to different issues, keyword-based approaches have been used previously, but these methods are solely dependent on seed keywords which are manually given and these set of keywords are not sufficient to cover all tweets posts. So, to overcome this issue, a novel approach has been proposed that captures the semantic context through dense word embedding by employing word2vec model. However, the process of tweet segregation on the basis of semantic similar keywords may suffer from the problem of pragmatic ambiguity. To handle this, Word2Vec model has been applied to match the semantically similar tweets with respect to each category. Furthermore, the hotspots have been identified corresponding to each category. However, due to the scarcity of geo-tagged tweets, we have proposed a hybrid method which amalgamates Named Entity Recognition (NER), Part of speech (POS), and Regular Expression (RE) to extract the location information from the tweet textual content. Due to the lack of availability of the ground truth dataset, model feasibility has been validated from the existing data records (i.e., published by government official accounts and reported on news media) and the evaluation results signify that the stated approach identifies few additional hotspots as compared to the existing reports while analyzing the tweets.
- Published
- 2019
- Full Text
- View/download PDF
30. Impact of Awareness Control on V2V-Based Overtaking Application in Autonomous Driving.
- Author
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Bolufe, Sandy, Azurdia-Meza, Cesar A., Cespedes, Sandra, and Montejo-Sanchez, Samuel
- Abstract
In autonomous driving, IEEE 802.11p-based vehicle-to-vehicle (V2V) communication is considered for overcoming the intrinsic limitations of sensors and supporting safety applications. In this letter, we evaluate the effectiveness of relevant awareness control approaches, such as ETSI DMG, IVTRC, and POSACC, to support the V2V-based overtaking application in autonomous driving. For this, we assess the incident detection capability of the overtaking application when it is running with messages gathered from these approaches, considering packet losses due to channel fading. Simulations show that POSACC is more effective than the remaining approaches for detecting unsafe overtaking maneuvers in different operating conditions. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
31. Multiple Sensors Data Integration for Traffic Incident Detection Using the Quadrant Scan
- Author
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Ayham Zaitouny, Athanasios D. Fragkou, Thomas Stemler, David M. Walker, Yuchao Sun, Theodoros Karakasidis, Eftihia Nathanail, and Michael Small
- Subjects
traffic monitoring ,traffic management ,non–recurrent congestion ,major/minor incident ,incident detection ,recurrence plots ,Chemical technology ,TP1-1185 - Abstract
Non-recurrent congestion disrupts normal traffic operations and lowers travel time (TT) reliability, which leads to many negative consequences such as difficulties in trip planning, missed appointments, loss in productivity, and driver frustration. Traffic incidents are one of the six causes of non-recurrent congestion. Early and accurate detection helps reduce incident duration, but it remains a challenge due to the limitation of current sensor technologies. In this paper, we employ a recurrence-based technique, the Quadrant Scan, to analyse time series traffic volume data for incident detection. The data is recorded by multiple sensors along a section of urban highway. The results show that the proposed method can detect incidents better by integrating data from the multiple sensors in each direction, compared to using them individually. It can also distinguish non-recurrent traffic congestion caused by incidents from recurrent congestion. The results show that the Quadrant Scan is a promising algorithm for real-time traffic incident detection with a short delay. It could also be extended to other non-recurrent congestion types.
- Published
- 2022
- Full Text
- View/download PDF
32. Road Data Enrichment Framework Based on Heterogeneous Data Fusion for ITS.
- Author
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Rettore, Paulo H. L., Santos, Bruno P., Rigolin F. Lopes, Roberto, Maia, Guilherme, Villas, Leandro A., and Loureiro, Antonio A. F.
- Abstract
In this work, we propose the Road Data Enrichment (RoDE), a framework that fuses data from heterogeneous data sources to enhance Intelligent Transportation System (ITS) services, such as vehicle routing and traffic event detection. We describe RoDE through two services: (i) Route service, and (ii) Event service. For the first service, we present the Twitter MAPS (T-MAPS), a low-cost spatiotemporal model to improve the description of traffic conditions through Location-Based Social Media (LBSM) data. As a case study, we explain how T-MAPS is able to enhance routing and trajectory descriptions by using tweets. Our experiments compare T-MAPS’ routes against Google Maps’ routes, showing up to 62% of route similarity, even though T-MAPS uses fewer and coarse-grained data. We then propose three applications, Route Sentiment (RS), Route Information (RI), and Area Tags (AT), to enrich T-MAPS’ suggested routes. For the second service, we present the Twitter Incident (T-Incident), a low-cost learning-based road incident detection and enrichment approach built using heterogeneous data fusion. Our approach uses a learning-based model to identify patterns on social media data which is then used to describe a class of events, aiming to detect different types of events. Our model to detect events achieved scores above 90%, thus allowing incident detection and description as a RoDE application. As a result, the enriched event description allows ITS to better understand the LBSM user’s viewpoint about traffic events (e.g., jams) and points of interest (e.g., restaurants, theaters, stadiums). [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
33. Optimized multistage fuzzy-based model for incident detection and management on urban streets.
- Author
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Hawas, Yaser E., Sherif, Mohammad, and Didarul Alam, Md.
- Subjects
- *
TRAFFIC monitoring , *STREETS , *TRAFFIC signs & signals , *CITY traffic , *SENSITIVITY analysis , *REGRESSION analysis - Abstract
This study proposes formulation of a system for incident detection and management of traffic signals constituting urban traffic networks. A system prototype has been developed and tested in a simulation environment under several incident scenarios. Following incident detection, the proposed system deploys a multistage fuzzy-logic model (FLM) to manage traffic signals. Details of FLM calibration have been presented and discussed. The proposed system has been calibrated under various traffic conditions and incident scenarios. A parametric sensitivity analysis was performed to optimize the proposed FLM, and further analysis has been performed to demonstrate robustness when tested under conditions different from those it has been optimized for, thereby leading to development of response surface methodology (RSM) models to determine the most robust parameters of FLM. RSM has been calibrated using the Box–Behnken design (BBD). Three different non-linear regression models have been used to identify those robust parameters concerning incident detection and traffic management that are likely to minimize the overall network travel time. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
34. Incident Detection in Freeway Based on Autocorrelation Factor of GPS Probe Data.
- Author
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Jalali, Ali and Torfeh Nejad, Hamid
- Abstract
This study proposes a statistical approach to incident detection in a section of the intercity freeway by applying GPS probe data to a GIS geofenced platform. We evaluated the proposed method using data sources from real traffic sensors of the intercity Tehran-Qom freeway in Iran. Through the SEPEHTAN project in Iran, intercity bus fleet equipped with an onboard unit that provides GPS data transferring to the central database. The main novelties in this paper are gathering density and speed time series from GPS probe data in a GIS platform and using autocorrelation factor to detect the location of the incident. The method compared with three different AID algorithms and real terms as well. Although the penetration rate was 3%, the results were considerably meet with the actual traffic condition. We reached 92.8% detection rate and 7.1% for the false alarm. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
35. Freeway incident detection based on set theory and short-range communication.
- Author
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Sun, Libing, Lin, Zhiting, Li, Wenna, and Xiang, Yaqin
- Subjects
- *
SET theory , *TRAFFIC safety , *EXPRESS highways , *TRAFFIC incident management , *ROAD safety measures , *FALSE alarms - Abstract
Freeway accidents cause grave fatalities and economic losses. Further losses of life and property can be caused by secondary accidents or rear-end chain accidents. Therefore, a quick and accurate incident detection technique is vital to improve the road safety and traffic management system performance. Based on the set theory and data collected by smartphones, a low-cost incident detection algorithm is proposed, called set theory-based freeway incident detection algorithm (SFID). If a vehicle is involved in a traffic incident, the capacity of the road segment in one direction declines significantly, whereas there are no changes in the opposite direction. SFID uses this phenomenon to infer if an incident has occurred or not. To evaluate the performance of SFID, simulations are conducted under different conditions. The results show a high detection rate, low average detection time, and low false alarm rate. Furthermore, the proposed scheme is evaluated using real Wi-Fi data. The results are in concordance with those of simulations. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
36. TrafficWatch: Real-Time Traffic Incident Detection and Monitoring Using Social Media
- Author
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Nguyen, Hoang, Liu, Wei, Rivera, Paul, Chen, Fang, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Bailey, James, editor, Khan, Latifur, editor, Washio, Takashi, editor, Dobbie, Gill, editor, Huang, Joshua Zhexue, editor, and Wang, Ruili, editor
- Published
- 2016
- Full Text
- View/download PDF
37. Non-Recurrent Congestion: Improvement of Time to Clear Incidents
- Author
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Gordon, Robert and Gordon, Robert
- Published
- 2016
- Full Text
- View/download PDF
38. Incidents1M: a Large-Scale Dataset of Images With Natural Disasters, Damage, and Incidents
- Author
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Weber, Ethan, Papadopoulos, Dim P., Lapedriza, Agata, Ofli, Ferda, Imran, Muhammad, Torralba, Antonio, Weber, Ethan, Papadopoulos, Dim P., Lapedriza, Agata, Ofli, Ferda, Imran, Muhammad, and Torralba, Antonio
- Abstract
Natural disasters, such as floods, tornadoes, or wildfires, are increasingly pervasive as the Earth undergoes global warming. It is difficult to predict when and where an incident will occur, so timely emergency response is critical to saving the lives of those endangered by destructive events. Fortunately, technology can play a role in these situations. Social media posts can be used as a low-latency data source to understand the progression and aftermath of a disaster, yet parsing this data is tedious without automated methods. Prior work has mostly focused on text-based filtering, yet image and video-based filtering remains largely unexplored. In this work, we present the Incidents1M Dataset, a large-scale multi-label dataset which contains 977,088 images, with 43 incident and 49 place categories. We provide details of the dataset construction, statistics and potential biases; introduce and train a model for incident detection; and perform image-filtering experiments on millions of images on Flickr and Twitter. We also present some applications on incident analysis to encourage and enable future work in computer vision for humanitarian aid. Code, data, and models are available at http://incidentsdataset.csail.mit.edu.
- Published
- 2023
39. Indoor Positioning and Fall Detection System Without Wearables: <redacted>
- Author
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Mulder, Gerben (author), Hernández Salvador, Kim (author), Baroud, Mounzir (author), Mulder, Gerben (author), Hernández Salvador, Kim (author), and Baroud, Mounzir (author)
- Abstract
This thesis report, one of a set of two reports, describes a novel way to detect incidents that could occur in the daily life of the elderly. Unlike most systems already implemented in this field, this system does not use any wearable (positioning) sensors and works off an Single Board Computer (SBC).Independent of both of these systems is a system for reassurance to alleviate distress., This is the public version of the report. The original report is shared with the jury., Electrical Engineering
- Published
- 2023
40. Traffic Incident Detection Based on Dynamic Graph Embedding in Vehicular Edge Computing
- Author
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Gen Li, Tri-Hai Nguyen, and Jason J. Jung
- Subjects
Ambient Intelligence ,dynamic graph embedding ,vehicular edge computing ,incident detection ,Internet of Things ,Technology ,Engineering (General). Civil engineering (General) ,TA1-2040 ,Biology (General) ,QH301-705.5 ,Physics ,QC1-999 ,Chemistry ,QD1-999 - Abstract
With a large of time series dataset from the Internet of Things in Ambient Intelligence-enabled smart environments, many supervised learning-based anomaly detection methods have been investigated but ignored the correlation among the time series. To address this issue, we present a new idea for anomaly detection based on dynamic graph embedding, in which the dynamic graph comprises the multiple time series and their correlation in each time interval. We propose an entropy for measuring a graph’s information injunction with a correlation matrix to define similarity between graphs. A dynamic graph embedding model based on the graph similarity is proposed to cluster the graphs for anomaly detection. We implement the proposed model in vehicular edge computing for traffic incident detection. The experiments are carried out using traffic data produced by the Simulation of Urban Mobility framework. The experimental findings reveal that the proposed method achieves better results than the baselines by 14.5% and 18.1% on average with respect to F1-score and accuracy, respectively.
- Published
- 2021
- Full Text
- View/download PDF
41. Intelligent algorithms for incident detection and management in smart transportation systems.
- Author
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Yijing, Huang, Wei, Wanyue, He, Yang, Qihong, Wu, and Kaiming, Xu
- Subjects
- *
INTELLIGENT transportation systems , *TRANSPORTATION management system , *GENERATIVE adversarial networks , *COMPUTER vision , *ARTIFICIAL intelligence , *DEEP learning - Abstract
Prior research on traffic event detection has encountered two problems: limited sample numbers and unbalanced datasets. Moreover, the real-time properties of event detection models must be enhanced to meet traffic management demands. To solve these issues, suitable measures must be implemented, like developing an intelligent algorithm for incident detection employing Artificial Intelligence (AI) and Machine Learning (ML). Automated Incident Detection (AID) methods are the focus of current Intelligent Transportation System (ITS) technology. Modern vehicles can connect with one other and with Roadside Infrastructure Units (RSUs) to improve road safety thanks to advancements in wireless connectivity and sensor technology. Deep Learning (DL)-based methods have recently demonstrated strong performance in computer vision issues involving complicated feature associations. This work proposes a Hybrid Deep Learning-based Automated Incident Detection and Management (HDL-AIDM) system to identify traffic incidents and improve traffic management. In the suggested model, a Temporal and Spatial Stacked Autoencoder (TSSAE) is used to collect temporal and spatial associations of traffic conditions and identify events. At the same time, a generative adversarial network (GAN) is employed to improve the number of samples and equalize datasets. The proposed model is assessed from many perspectives using the dataset from real-world situations. Based on the HDL output for AID, an efficient and intelligent traffic management algorithm has been accomplished using Road Side Units (RSUs) to gather traffic information. The suggested incident management algorithm considers lane shifts and the fluctuation in vehicle speed over time, which are heavily influenced by traffic incidents. These developments in ITS enable traffic management systems to use information gathered from HDL-based AID methods using TSSAE and GAN. The proposed strategies have been devised to notify drivers about traffic issues and help them avoid congestion. The suggested method provides higher incident detection rates with an accuracy of 94.1%, a 3.9% false alarm rate, and an incident classification rate of 93.3%. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
42. A hybrid model using logistic regression and wavelet transformation to detect traffic incidents
- Author
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Shaurya Agarwal, Pushkin Kachroo, and Emma Regentova
- Subjects
Incident detection ,Wavelet analysis ,Logistic regression ,Transportation and communications ,HE1-9990 - Abstract
This research paper investigates a hybrid model using logistic regression with a wavelet-based feature extraction for detecting traffic incidents. A logistic regression model is suitable when the outcome can take only a limited number of values. For traffic incident detection, the outcome is limited to only two values, the presence or absence of an incident. The logistic regression model used in this study is a generalized linear model (GLM) with a binomial response and a logit link function. This paper presents a framework to use logistic regression and wavelet-based feature extraction for traffic incident detection. It investigates the effect of preprocessing data on the performance of incident detection models. Results of this study indicate that logistic regression along with wavelet based feature extraction can be used effectively for incident detection by balancing the incident detection rate and the false alarm rate according to need. Logistic regression on raw data resulted in a maximum detection rate of 95.4% at the cost of 14.5% false alarm rate. Whereas the hybrid model achieved a maximum detection rate of 98.78% at the expense of 6.5% false alarm rate. Results indicate that the proposed approach is practical and efficient; with future improvements in the proposed technique, it will make an effective tool for traffic incident detection.
- Published
- 2016
- Full Text
- View/download PDF
43. A Machine Vision Based Surveillance System For California Roads
- Author
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Malik, J. and Russell, S.
- Subjects
Computer vision ,Image processing ,Automobiles--Automatic control ,Traffic surveillance ,Incident detection ,Automatic vehicle classification - Abstract
In this paper, the authors describe the successful combination of a low- level, vision-based surveillance system with a high-level, symbolic reasoner based on dynamic belief networks. This prototype system provides robust, high-level information about traffic scenes. The machine vision component of the system employs a correlation-based tracker and a physical motion model using a Kalman filter to extract vehicle trajectories over a sequence of traffic scene images. The symbolic reasoning component uses a dynamic belief network to make inferences about traffic events. In this paper, the authors discuss the key tasks of the vision and reasoning components as well as their integration into a working prototype.
- Published
- 1995
44. Detecting Traffic Incidents Using Persistence Diagrams
- Author
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Eric S. Weber, Steven N. Harding, and Lee Przybylski
- Subjects
persistence diagram ,bottleneck distance ,anomaly detection ,bagging ,incident detection ,Industrial engineering. Management engineering ,T55.4-60.8 ,Electronic computers. Computer science ,QA75.5-76.95 - Abstract
We introduce a novel methodology for anomaly detection in time-series data. The method uses persistence diagrams and bottleneck distances to identify anomalies. Specifically, we generate multiple predictors by randomly bagging the data (reference bags), then for each data point replacing the data point for a randomly chosen point in each bag (modified bags). The predictors then are the set of bottleneck distances for the reference/modified bag pairs. We prove the stability of the predictors as the number of bags increases. We apply our methodology to traffic data and measure the performance for identifying known incidents.
- Published
- 2020
- Full Text
- View/download PDF
45. Network Traffic Screening Using Frequent Sequential Patterns
- Author
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Tsuruta, Hisashi, Shoudai, Takayoshi, Takeuchi, Jun’ichi, Ao, Sio Iong, editor, Castillo, Oscar, editor, and Huang, Xu, editor
- Published
- 2012
- Full Text
- View/download PDF
46. Experiences with Video-Based Incident Detection and Parking Space Surveillance Systems on Motorways in the Free State of Saxony
- Author
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Döge, Klaus-Peter and Mikulski, Jerzy, editor
- Published
- 2011
- Full Text
- View/download PDF
47. Incident Detection in Urban Road
- Author
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Bing-Fei, Wu, Chih-Chung, Kao, Chao-Jung, Chen, Yen-Feng, Li, Ying-Han, Chen, Cheng-Yen, Yang, Zeng, Zhigang, editor, and Wang, Jun, editor
- Published
- 2010
- Full Text
- View/download PDF
48. Extended Floating Car Data in Co-operative Traffic Management
- Author
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Scheider, Thomas, Böhm, Martin, Barceló, Jaume, editor, and Kuwahara, Masao, editor
- Published
- 2010
- Full Text
- View/download PDF
49. Non-Recurrent Congestion: Improvement of Time to Clear Incidents
- Author
-
Gordon, Robert L. and Gordon, Robert L.
- Published
- 2010
- Full Text
- View/download PDF
50. Automated Log Analysis of Infected Windows OS Using Mechanized Reasoning
- Author
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Ando, Ruo, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Leung, Chi Sing, editor, Lee, Minho, editor, and Chan, Jonathan H., editor
- Published
- 2009
- Full Text
- View/download PDF
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